Survival analysis in Python, including Kaplan Meier, Nelson Aalen and regression
Project Links
Meta
Author: Cameron Davidson-Pilon
Requires Python: >=3.9
Classifiers
Development Status
- 4 - Beta
License
- OSI Approved :: MIT License
Programming Language
- Python
- Python :: 3.9
- Python :: 3.10
- Python :: 3.11
Topic
- Scientific/Engineering
What is survival analysis and why should I learn it? Survival analysis was originally developed and applied heavily by the actuarial and medical community. Its purpose was to answer why do events occur now versus later under uncertainty (where events might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions like what factors might influence deaths?
But outside of medicine and actuarial science, there are many other interesting and exciting applications of survival analysis. For example:
- SaaS providers are interested in measuring subscriber lifetimes, or time to some first action
- inventory stock out is a censoring event for true "demand" of a good.
- sociologists are interested in measuring political parties' lifetimes, or relationships, or marriages
- A/B tests to determine how long it takes different groups to perform an action.
lifelines is a pure Python implementation of the best parts of survival analysis.
Documentation and intro to survival analysis
If you are new to survival analysis, wondering why it is useful, or are interested in lifelines examples, API, and syntax, please read the Documentation and Tutorials page
Contact
- Start a conversation in our Discussions room.
- Some users have posted common questions at stats.stackexchange.com.
- Creating an issue in the Github repository.
Development
See our Contributing guidelines.
0.30.0
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Wheel compatibility matrix
Files in release
Extras:
None
Dependencies:
numpy
(>=1.14.0)
scipy
(>=1.7.0)
pandas
(>=2.1)
matplotlib
(>=3.0)
autograd
(>=1.5)
autograd-gamma
(>=0.3)
formulaic
(>=0.2.2)